There are several fields of research studying acoustic signal enhancement, with the emphasis being on speech signals. Among those are: voice communication, automatic speech recognition (ASR), and hearing aids. Each field of research has adopted its own approaches to acoustic signal enhancement, with some overlap between them.
Acoustic signals are often degraded by the presence of noise. For example, in a busy office or a moving automobile, the performance of ASR systems degrades substantially. If voice is transmitted to a remote listener—as in a teleconferencing system—the presence of noise can be annoying or distracting to the listener, or even make the speech difficult to understand. People with a loss of hearing have notable difficulty understanding speech in noisy environment, and the overall gain applied to the signal by most current hearing aids does not help alleviate the problem. Old music recordings are often degraded by the presence of impulsive noise or hissing. Other examples of communication where acoustic signal degradation by noise occurs include telephony, radio communications, video-conferencing, computer recordings, etc.
Continuous speech large vocabulary ASR is particularly sensitive to noise interference, and the solution adopted by the industry so far has been the use of headset microphones. Noise reduction is obtained by the proximity of the microphone to the mouth of the subject (about one-half inch), and sometimes also by special proximity effect microphones. However, a user often finds it awkward to be tethered to a computer by the headset, and annoying to be wearing an obtrusive piece of equipment. The need to use a headset precludes impromptu human-machine interactions, and is a significant barrier to market penetration of ASR technology.
Apart from close-proximity microphones, traditional approaches to acoustic signal enhancement in communication have been adaptive filtering and spectral subtraction. In adaptive filtering, a second microphone samples the noise but not the signal. The noise is then subtracted from the signal. One problem with this approach is the cost of the second microphone, which needs to be placed at a different location from the one used to pick up the source of interest. Moreover, it is seldom possible to sample only the noise and not include the desired source signal. Another form of adaptive filtering applies bandpass digital filtering to the signal. The parameters of the filter are adapted so as to maximize the signal-to-noise ratio (SNR), with the noise spectrum averaged over long periods of time. This method has the disadvantage of leaving out the signal in the bands with low SNR.
In spectral subtraction, the spectrum of the noise is estimated during periods where the signal is absent, and then subtracted from the signal spectrum when the signal is present. However, this leads to the introduction of “musical noise” and other distortions that are unnatural. The origin of those problems is that, in regions of very low SNR, all that spectral subtraction can determine is that the signal is below a certain level. By being forced to make a choice of signal level based on sometimes poor evidence, a considerable departure from the true signal often occurs in the form of noise and distortion.
A recent approach to noise reduction has been the use of beamforming using an array of microphones. This technique requires specialized hardware, such as multiple microphones, A/D converters, etc., thus raising the cost of the system. Since the computational cost increases proportionally to the square of the number of microphones, that cost also can become prohibitive. Another limitation of microphone arrays is that some noise still leaks through the beamforming process. Moreover, actual array gains are usually much lower than those measured in anechoic conditions, or predicted from theory, because echoes and reverberation of interfering sound sources are still accepted through the mainlobe and sidelobes of the array.
The inventor has determined that it would be desirable to be able to enhance an acoustic signal without leaving out any part of the spectrum, introducing unnatural noise, or distorting the signal, and without the expense of microphone arrays. The present invention provides a system and method for acoustic signal enhancement that avoids the limitations of prior techniques.